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Examples of the interoperability between data mining and spatial components in the context of processing satellite sensor data are given in the satellite imagery case study. BY location Analytic Module CREATE CONTINUOUS QUERY sensor_change_cq COMPUTE ON COMMIT DESTINATION change_table SELECT location, time_stamp, measurement FROM (SELECT location, time_stamp, meaLAG(measurement, 1) OVER (PARTITION ORDER BY time) prev_measurement FROM sensor_measurements) WHERE prev_measurement – measurement > 100; The ETL stage can also include more sophisticated data mining approaches that extract information from the data stream.

See (Cuzzocrea, 2005)). Overall, in this chapter we introduce an innovative, complex technique for efficiently supporting OLAP analysis of multidimensional data streams. We highlight since here that our proposed representation and analysis models are indeed general enough to deal with data streams generated by any source of intermittent data, regardless from the particular application scenario considered in this chapter and represented by data streams generated by sensor networks. Figure 1 provides a comprehensive overview of our technique.

SVM in Oracle Database 10g: Removing the barriers to widespread adoption of support vector machines. In Proceedings of the 31st International Conference on Very Large Data Bases (pp. 1152-1163). , & O’Callaghan, L. (2003). Clustering data streams: Theory and practice. IEEE Transactions on Knowledge and Data Engineering, 15(3), 515–528. , & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the Seventh ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (pp.

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